The invention is an integrated circuit (IC) suitable: for use in real-time multiresolution signal processing apparatus. The IC is useful for analyzing the contents and the frequency spectrum of an information bearing signal and for compressing the signal to represent the information it conveys in a more compact form.
Multiresolution or pyramid representation of data has been widely applied for processing image data for many applications such as image coding and surveillance. The technique has also been recognized as being applicable to other signals containing redundant information, such as audio signals.
Multiresolution image processing involves recursive or iterative generation of both high-pass filtered and a low-pass filtered components of data in a single or multi-dimensional space. For a multidimensional signal, a single step in this iterative procedure may, for example, apply the input signal to a multi-dimensional low-pass filter to generate the low-pass filtered output signal. This output signal may then be subtracted from the delayed input signal to produce the high-pass filtered output signal.
In an iterative or recursive application of this process, the high-pass filtered data is saved and the procedure described above is applied to the low-pass filtered output signal. This low-pass filtered signal can be represented by a smaller number of samples. If the samples of the high-pass filtered output signals were arranged in layers, with the first high-pass filtered signal on the bottom and successive high-pass filtered signals in increasingly higher layers, the resulting structure would resemble a triangle for one dimensional data or a pyramid for two dimensional data.
The high-pass filtered data sets resulting from this operation represents signal information in different bands of frequencies. For image data, these would be spatial frequencies while for audio data they would be audio frequencies.
As applied to image analysis, the high-pass filtered signals approximate the Laplacian of the image at respectively different levels of resolution. It is well known that data of this type is useful for finding and classifying details such as edges in an image (i.e. correlated data values) and for determining the level of noise in an image (i.e. uncorrelated data values). In addition, the spatial frequency information provided by the different levels of the pyramid may be used to identify objects in the image which have known spatial frequency spectra.
For image compression, the range of significant values represented by the high pass filtered image is substantially less than that represented by the unfiltered image. Thus, significant data reduction can be achieved by variably quantizing the high-pass filtered image data using, for example, a Huffman-type code. In addition, the low-pass filtered data may be represented by a smaller number of sample values than the original data. For a one-octave reduction in signal frequency, the number of samples needed to represent a low-pass filtered linear (one-dimensional) signal is one-half of that used for the original signal and one-quarter for a planar (two-dimensional) signal. For two-dimensional image data combined compression ratios of approximately 8:1 with very little loss in visual quality are common.
A system for pyramid processing is described in U.S. Pat. No. 4,674,125 to Carlson et al., incorporated herein by reference for its teachings on multiresolution signal processing techniques. Carlson et al.'s system is a pipelined digital signal processing system which filters images through multiple pyramid stages in real time with a delay. That system, however, may be relatively expensive and difficult to implement. For example, the two-dimensional low-pass filters used in this system each use multiple digital multiplier circuits. These circuits have a relatively large number of components and, thus, are relatively expensive. In addition, the designer of such a circuit would need to closely control the propagation delays through each filter stage in the pipeline so that the signal could presented to the following stage within the timing constraints appropriate for that circuit. This attention to timing details can add significant complexity to the circuit design.
Another pyramid processing system is described in U.S. Pat. No. 4,703,514 to G. van der Wal, incorporated herein by reference for its teachings on multiresolution signal processing techniques. van der Wal's system may be used to implement a pyramid processor in a single-stage non-real-time system or in a multi-stage delayed real-time system. The filter shown in van der Wal's FIG. 3, includes a filter logic unit module having an m by m two-dimensional digital filter and supporting circuitry which accepts two input signals and provides two output signals. This module may be programmed to simultaneously provide a Laplacian high-pass filtered image and a Gaussian low-pass filtered image of a single input signal. Since this circuit has two input terminals, and programmable filter coefficients, it may also be used to combine Laplacian images produced by a pyramid analyzer to regenerate the original image.
While this system is more flexible than the system disclosed by Carlson et al., it has many of the same problems. It uses the same two-dimensional filter as the Carlson et al. system and is subject to the same timing constraints. Thus, any multi-stage filter implemented using van der Wal's approach is likely to be complex and relatively expensive. The IC disclosed herein performs similar functions to van der Wal's system in a less complex and less expensive way with added functionality.